The gradients \(\mathbf {G}\)  in typical federated learning settings are the minimum that must be shared to the server, corresponding to FedSGD approach. In FedAVG [1]}, models are consecutively updated on more batches of local data, which can be several epochs of training, and then shared. We note that a common way is to share the updated model \(\mathbf {\theta }+\mathbf {G}\) , but this practically amounts to sharing \(\mathbf {G}\)  since all participants know \(\mathbf {\theta }\) . Besides, the gradient sharing can be among clients and the federated server in any direction depending on different cases [2]}.
